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we present adversarial water marking transformer towards tracing text provenance with data hiding recent language models have shown tremendous success in producing high quality text this raised docHub concerns about the potential abuse of such models such as creating news and misinformation current classifiers might not be generalizable and sustainable to future technologies therefore there is a need to move from passive to active defenses against the s language watermarking is an active and sustainable solution to mark and trace the output of language models in this scenario the owner water marks the output of a black box api which can be later misused by malicious users the watermark can be detected from potential measures content this enables the responsible release and potential regulation of text generation technologies towards the goal we propose awt as the first learned and end-to-end framework for language watermarking our model achieves more natural changes and higher rob
